Apparatus and method for generating coefficients, apparatus and method for generating class configuration, informational signal processing apparatus, and programs for performing these methods

ABSTRACT

A class configuration generation unit generates (n−1) number of class configurations each of which is comprised of i number of the already selected features plus a feature selected from the remaining (n−i) number of the features (both of n and i are integers). A class configuration selection unit selects an optimal class configuration from the (n−i) number of the class configurations using an arbitrary evaluation value. The features used in the class configuration selected by the selection unit are used as the already selected features in the generation unit. The operations by the generation unit and the selection unit are repeated with values of i sequentially varying from 0 to r−1, thereby generating a class configuration comprised of r number of the features.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to an apparatus and method for generatinga coefficient, an apparatus and method for generating a classconfiguration, an informational signal processing apparatus, andprograms for performing these methods. More specifically, it relates toan apparatus and method for generating a coefficient and the likesuitable for being well applied to an apparatus for converting astandard TV signal (SD signal) into a high-resolution signal (HD signal)and the like.

2. Description of Related Art

In recent years, a variety of technologies have been proposed forimproving a resolution or a sampling frequency of an image or audiosignal. For example, it is known that in a case where a standard TVsignal suited to a standard or low resolution is upgraded to ahigh-resolution signal, a so-called HDTV signal or where it undergoessub-sample interpolation, conversion processing accompanied by classcategorization gives a better result in performance than an approach bymeans of conventional linear interpolation.

According to this conversion processing accompanied by the classcategorization, for example, in the case of converting a standard TVsignal (SD signal) suited to a standard or low resolution into ahigh-resolution signal (HD signal), a class to which pixel data of atarget position in the HD signal belongs is detected from apredetermined class configuration, so that using coefficient data for anestimation equation that corresponds to this class, the pixel data ofthe target position in the HD signal is generated from multiple items ofpixel data of the SD signal based on this estimation equation. Thecoefficient data for the estimation equation used in this conversionprocessing accompanied by the class categorization is determined byperforming learning such as least-squares method beforehand for eachclass.

However, to perform this conversion processing accompanied by classcategorization, a class configuration (a combination of features)required to perform class categorization must be determined. Althoughgenerally the performance becomes better as the features are used more,an amount of the coefficient data or coefficient seed data which iscoefficient data of a generation equation for generating thiscoefficient data may become enormous, or the calculation therefor mayinvolve an immense amount of time. To solve this problem, it isimportant to determine an appropriate class configuration.

To determine the class configuration, it has conventionally beennecessary to consider a few class configuration candidates obtainedthrough human experiences in the past, perform learning separately foreach class configuration, and select a seemingly best one of the classconfigurations based on a result of the leaning. Therefore, the humanexperiences are always relied on and the learning is always repeatedfrom the beginning for each time the class configuration is changed,thus resulting in enormous time required for that.

It is an object of the present invention to efficiently generatecoefficient data etc. for each class in an arbitrary class configurationby performing learning only once. It is another object of the presentinvention to obtain an optimal class configuration in short time withoutrelying on human experiences. It is a further object of the presentinvention to convert a first informational signal into a secondinformational signal by performing conversion processing accompanied byclass categorization by use of an optimal configuration.

SUMMARY OF THE INVENTION

According to an aspect of the invention, there provides a coefficientgeneration apparatus for generating coefficient data for an estimationequation which is used for converting a first informational signalcomprised of multiple items of informational data into a secondinformational signal comprised of multiple items of informational dataor coefficient seed data that is coefficient data in a generationequation for generating the coefficient data for the estimationequation. The apparatus comprises a storage unit for storing a normalequation for calculating the coefficient data for the estimationequation or the coefficient seed data for each class in a basic classconfiguration comprised of all of plural features. The apparatus alsocomprises a normal equation generation unit for, based on information ofa target class configuration comprised of arbitrary one or more featuresof the plural features, generating a normal equation for calculating thecoefficient data for the estimation equation or the coefficient seeddata for each class in the target class configuration. The apparatusfurther comprises a calculation unit for solving the normal equation,which is generated by the normal equation generation unit, and forcalculating the coefficient data for the estimation equation or thecoefficient seed data for each class in the target class configurationto calculate for each class the coefficient data for the estimationequation or the coefficient seed data.

According to another aspect of the invention, there provides acoefficient generation method for generating coefficient data for anestimation equation which is used for converting a first informationalsignal comprised of multiple items of informational data into a secondinformational signal comprised of multiple items of informational dataor coefficient seed data that is coefficient data in a generationequation for generating the coefficient data for the estimationequation. The method comprises the step of preparing a normal equationfor calculating the coefficient data for the estimation equation or thecoefficient seed data for each class in a basic class configurationcomprised of all of plural features. The method also comprises the stepof generating a normal equation for calculating the coefficient data forthe estimation equation or the coefficient seed data for each class inthe target class configuration, based on information of a target classconfiguration comprised of at least arbitrary one of the pluralfeatures. The method further comprises the step of solving the generatednormal equation for calculating the coefficient data for the estimationequation or the coefficient seed data for each class in the target classconfiguration to calculate for each class the coefficient data for theestimation equation or the coefficient seed data for each class in thetarget class configuration. Further, according to an additional aspectof the invention, there provides a program for commanding a computer toexecute the above coefficient generation method.

In the present invention, the coefficient data for an estimationequation which is used for converting first informational signalcomprised of multiple items of informational data into a secondinformational signal comprised of multiple items of informational dataor coefficient seed data (coefficient data etc.) that is coefficientdata in a generation equation for generating the coefficient data forthe estimation equation is generated. These informational signals areeach, for example, an image signal or an audio signal.

A normal equation for calculating the coefficient data or thecoefficient seed data of each class in a basic class configurationcomprised of all of plural features is prepared. This normal equation isobtained by performing learning for each of the classes beforehand.Next, based on information of a target class configuration comprised ofarbitrary one or more features of the plural features, the normalequation for calculating the coefficient data etc. for each class in thetarget class configuration is generated.

Based on the information of the target class configuration, only thefeature (s) included in the target class configuration may be consideredto detect such a class in the basic class configuration as to have thesane feature, thereby detecting the class in the basic classconfiguration that corresponds to each class in the target classconfiguration. Then, normal equations of the detected classes in thebasic class configuration that correspond to each class in the targetclass configuration are added up for each class, thereby obtaining anormal equation for calculating coefficient data etc. for each class inthe target class configuration.

Further, if n number of the features are included in the basic classconfiguration and each class in this basic class configuration isindicated by n-bit data whose each bit indicates the feature, n-bit maskbit pattern data whose a bit corresponding to any one of the featuresincluded in the target class configuration is set to “1” is generated. Alogical product of the n-bit data representing each class in the basicclass configuration and this mask bit pattern data is calculated foreach bit. The classes having the same bit pattern as a calculationresult in the basic class configuration are categorized into the samegroup, thereby detecting such the classes in the basic classconfiguration as to correspond to each class in the target classconfiguration.

Each of these generated normal equations for calculating coefficientdata etc. for each class in the target class configuration is solved,thereby obtaining the coefficient data etc. for each class in the targetclass configuration.

In such a manner, according to the present invention, it is possible toefficiently generate the coefficient data etc. of each class in a targetclass configuration comprised of arbitrary one or more features of theplural features by performing the learning only once.

More specifically, by preparing a normal equation for calculating thecoefficient data etc. for each class in a basic class configurationcomprised of all of plural features, considering only the featuresincluded in the target class configuration, detecting such classes inthe basic class configuration as to have the same feature, and adding uptheir normal equations to generate a normal equation for calculating thecoefficient data etc. for each class in the target class configuration,coefficient data etc. for each class in an arbitrary class configurationare efficiently generated by performing the learning only once.Therefore, to alter the features included in a target classconfiguration, it is unnecessary to perform learning again, therebyenabling coefficient data to be easily generated in short time.

According to further aspect of the present invention, there provides aclass configuration generation apparatus for selecting r number offeatures from n number of the features, both of n and r being integers,r<n, to obtain a class configuration which is used for generating,through class categorization, informational data of a target position ina second informational signal comprised of multiple items ofinformational data when converting a first informational signalcomprised of multiple items of informational data into the secondinformational signal. The class configuration generation apparatuscomprises a class configuration generation unit for generating (n−i)number of class configurations each of which is comprised of i (which isan integer) number of the already selected features plus a featureselected from the remaining (n−i) number of the features. The classconfiguration generation apparatus further comprises a classconfiguration selection unit for selecting an optimal classconfiguration from the (n−i) number of the class configurationsgenerated by this class configuration generation unit, using anarbitrary evaluation value. In this class configuration generationapparatus, the features used in the class configuration selected by theclass configuration selection unit are set as the already selectedfeatures, and operations by the class configuration generation unit andthe class configuration selection unit are repeated with values for saidi sequentially varying from 0 to r−1, thereby obtaining a classconfiguration comprised of the r number of the features.

According to still further aspect of the present invention, thereprovides a class configuration generation method for selecting r numberof features from n number of the features, both of n and r beingintegers, r<n, to obtain a class configuration which is used forgenerating, through class categorization, informational data of a targetposition in a second informational signal comprised of multiple items ofinformational data when converting a first informational signalcomprised of multiple items of informational data into the secondinformational signal. The method comprises a class configurationgeneration step of generating (n−i) number of class configurations eachof which is comprised of already selected i (which is an integer) numberof features plus a feature selected from the remaining (n−i) number ofthe features. The method also comprises a class configuration selectionstep of selecting an optimal class configuration from the (n−i) numberof the class configurations generated by this class configurationgeneration step, using an arbitrary evaluation value. In this method,the features used in the class configuration selected by the classconfiguration selection step are set as the already selected features,and operations by the class configuration generation unit and the classconfiguration selection unit are repeated with values for said isequentially varying from 0 to r−1, thereby obtaining a classconfiguration comprised of the r number of the features.

A program related to the present invention causes a computer to executethis class configuration generation method.

According to the present invention, a class configuration which is usedfor generating, through class categorization, informational data of atarget position in a second informational signal comprised of multipleitems of informational data when converting a first informational signalcomprised of multiple items of informational data into the secondinformational signal is obtained by selecting r number of the featuresfrom n number of the features.

The feature(s) used in the selected class configuration is (are) set asthe already selected features. An operation of generating (n−i) numberof class configurations each of which is comprised of i number of thealready selected features selected from remaining (n−i) number of thefeatures and an operation of selecting an optimal class configurationfrom the (n−i) number of the class configurations using an arbitraryevaluation value are repeated with values for the i sequentially varyingfrom 0 to r−1, thereby enabling a class configuration comprised of the rnumber of the features to be obtained.

The informational data of a target position in the second informationalsignal is generated using coefficient data for an estimation equationthat corresponds to a class to which the informational data of thistarget position belongs and based on this estimation equation. Forexample, an optimal class configuration is selected as follows.

That is, coefficient data for each class in (n−i) number of thegenerated class configurations, respectively, is generated. Next, foreach class configuration, informational signal, which is made from anevaluating informational signal that corresponds to the secondinformational signal, corresponding to the first informational signal isconverted to an informational signal corresponding to the secondinformational signal using the generated coefficient data. Next, foreach class configuration, an evaluation value is calculated on the basisof a difference for each item of informational data between theinformational signal thus obtained by the conversion and the evaluationinformational signal. Then, based on the evaluation value of each ofthese class configurations, an optimal class configuration is selected.

In this case, when obtaining coefficient data for each class in the(n−i) number of the respective generated class configurations, using acoefficient generation apparatus or method for utilizing a normalequation for calculating the coefficient data etc. for each class in abasic class configuration comprised of all of plural features asdescribed above allows the coefficient data etc. for each class in the(n−i) number of the respective generated class configurations to beobtained by performing learning one time only, thereby improving anefficiency of processing.

In such a manner, according to the present invention, an operation ofgenerating (n−i) number of class configurations each of which iscomprised of i number of the already selected features plus a featureselected from the remaining (n−i) number of the features and anoperation of selecting an optimal class configuration from the (n−i)number of class configurations using an arbitrary evaluation value arerepeated with the values for i sequentially varying from 0 to r−1 inwhich the features used in the selected class configuration are set asthe already selected features, to obtain a class configuration comprisedof the r number of the features, thereby enabling an optimal classconfiguration in short time without relying on human experiences.

According to additional aspect of the present invention, there providesan informational signal processing apparatus for converting a firstinformational signal comprised of multiple items of informational datainto a second informational signal comprised of multiple items ofinformational data. The informational signal processing apparatuscomprises a class detection unit for detecting, based on the firstinformational signal, a class in a predetermined class configuration towhich informational data of a target position in the secondinformational signal belongs. The informational signal processingapparatus also comprises an informational data generation unit forgenerating the informational data of the target position in the secondinformational signal in accordance with the class detected by this classdetection unit.

The predetermined class configuration is comprised of r (which is aninteger) number of the features selected from n (which is an integer,r<n) number of the features. The r number of the features is obtained byrepeating an operation of generating (n−i) number of classconfigurations each of which is comprised of i (which is an integer)number of the already selected features plus a feature selected from theremaining (n−i) number of the features, and an operation of selecting anoptimal class configuration from the (n−i) number of the generated classconfigurations using an arbitrary evaluation value, with values for thei sequentially varying from 0 to r−1, in which the features used in theselected class configuration are set as the already selected features.

According to the present invention, a first informational signalcomprised of multiple items of informational data is converted into asecond informational signal comprised of multiple items of informationaldata. In this case, based on the first informational signal, a class ina predetermined class configuration to which informational data of atarget position in the second informational signal belongs is detected,so that corresponding to this detected class, the informational data ofthe target position in the second informational signal is generated.

For example, the informational data of the target position in the secondinformational signal is generated as follows. That is, coefficient data,which is used in an estimation equation, corresponding to the detectedclass is generated. Based on the first informational signal, multipleitems of informational data positioned in the periphery of a targetposition in the second informational signal are selected. Then, thegenerated coefficient data and multiple items of the selectedinformational data are used to calculate the informational data of thetarget position in the second informational signal based on theestimation equation.

It is to be noted that the predetermined class configuration iscomprised of r number of the features selected from n number of thefeatures, which r number of the features have been obtained as follows.That is, the r number of the features are obtained by repeating anoperation of generating (n−i) number of class configurations each ofwhich is comprised of i (which is an integer) number of the alreadyselected features plus a feature selected from the remaining (n−i)number of the features and an operation of selecting an optimal classconfiguration from (n−i) number of the generated class configurationsusing an arbitrary evaluation value, with values for i sequentiallyvarying from 0 to r−1, in which the features used in the selected classconfiguration are set as the already selected features.

In such a manner, according to the present invention, performingconversion processing accompanied by class categorization by use of anoptimal class configuration comprised of r number of the featuresselected from n number of the features allows the first informationalsignal to be converted into the second informational signal, therebyobtaining the second informational signal well.

The concluding portion of this specification particularly points out anddirectly claims the subject matter of the present invention. Howeverthose skill in the art will best understand both the organization andmethod of operation of the invention, together with further advantagesand objects thereof, by reading the remaining portions of thespecification in view of the accompanying drawing(s) wherein likereference characters refer to like elements.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram for showing a configuration of an image signalprocessing apparatus;

FIG. 2 is a diagram for showing a positional relationship in pixelsbetween a 525i signal and a 1050i signal;

FIG. 3 is a diagram for showing a class tap;

FIG. 4 is a diagram for showing a dynamic-range DR class;

FIG. 5 is a diagram for showing bit pattern data of a basic classconfiguration;

FIG. 6 is a diagram for showing a phase shift (in an odd-numberedfield), with respect to a predicted center tap, of four pixels in a unitpixel block of an HD signal;

FIG. 7 is a diagram for showing phase shift (in an even-numbered field),with respect to a predicted center tap, of four pixels in a unit pixelblock of an HD signal;

FIG. 8 is a block diagram for showing a configuration of a normalequation generation apparatus;

FIG. 9 is a block diagram for showing a configuration of coefficientgeneration apparatus;

FIG. 10A is a diagram for showing one example of each class in the basicclass configuration in processing of detecting a class in the basicclass configuration that corresponds to each class in a target classconfiguration;

FIG. 10B is a diagram for showing one example of mask bit pattern dataMBP in processing of detecting the class in the basic classconfiguration that corresponds to each class in the target classconfiguration;

FIG. 10C is a diagram for showing one example of a result of calculationof a logical product in processing of detecting the class in the basicclass configuration that corresponds to each class in the target classconfiguration;

FIG. 11 is a flowchart for showing a procedure of processing forgenerating a target class configuration;

FIG. 12 is a block diagram for showing a configuration of the targetclass configuration generation apparatus;

FIG. 13 shows an example of the processing for generating a target classconfiguration;

FIG. 14 is a block diagram for showing a configuration of the imagesignal processing apparatus to be realized by software;

FIG. 15 is a flowchart for showing image signal processing;

FIG. 16 is a flowchart for showing processing for generating a normalequation;

FIG. 17 is a flowchart for showing processing for generating acoefficient;

FIG. 18 is a block diagram for showing a configuration of another imagesignal processing apparatus;

FIG. 19 is a diagram for explaining a method for generating a normalequation for coefficient seed data;

FIG. 20 is a block diagram for showing a configuration of another normalequation generation apparatus;

FIG. 21 is a flowchart for showing the image signal processing; and

FIG. 22 is a flowchart for generating the normal equation.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

The following will describe a first embodiment of the invention. FIG. 1shows a configuration of an image signal processing apparatus 100 ofinformational signal processing apparatuses. The image signal processingapparatus 100 handles an image. This image signal processing apparatus100 converts a standard definition (SD) signal referred to as a 525isignal into a high definition (HD) signal referred to as a 1050i signal.

FIG. 2 shows a relationship between the 525i signal and the 1050i signalin terms of pixel position of a frame where these signals are present.In FIG. 2, the pixel position of an odd-numbered (o) field is indicatedby a solid line and that of an even-numbered (e) field is indicated by abroken line. A large dot represents a pixel of the 525i signal and asmall dot represents a pixel of the 1050i signal. As can be seen fromFIG. 2, items of pixel data of the 1050i signal may come in items ofline data L1 and L1′ positioned near a line of the 525i signal and itemsof line data L2 and L2′ positioned far away from the line of the 525isignal.

It is to be noted that L1 and L2 signify line data of an odd-numberedfield and L1′ and L2′ signify line data of an even-numbered field.Further, the number of pixels of each line of the 1050i signal is twicethat of the 525i signal.

As shown in FIG. 1, the image signal processing apparatus 100 comprisesan input terminal 101 for receiving the SD signal and first and secondtap selection circuits 102 and 103 each of which selectively takes out,based on the SD signal received by this input terminal, data of pluralSD pixels positioned in the periphery of a target position in the HDsignal and outputs the data.

The first tap selection circuit 102 selectively takes out data of pluralSD pixels that is used in prediction (refereed to as “prediction taps”).The second tap selection circuit 103 selectively takes out data ofplural SD pixels that is used in categorization of classes (referred toas “class taps”).

Further, the image signal processing apparatus 100 comprises a classdetection circuit 104 for detecting a class to which pixel data of atarget position in the HD signal belongs, from the data of class tapstaken out selectively by the second tap selection circuit 103.

In the present t, as shown in FIG. 3, data of five SD pixels positionedin the periphery of a target position 30 in the HD signal is taken outas the data of class taps and used. In FIG. 3, “x” indicates each of thetarget positions 30 in the HD signal and “0” through “4” each indicate atap position number. In this case, corresponding to the four targetpositions, the items of data of the five identical SD pixels are takenout as the data of class taps.

In the present embodiment, the class detection circuit 104 detects aclass CL in a target class configuration comprised of, for example, sixfeatures that are selected from 12 features included in a basic classconfiguration.

The following will describe the 12 features included in the basic classconfiguration. Some of these 12 features are pixel-value features andthe others are dynamic-range (DR) features.

Each pixel-value feature is composed of a 2-bit code obtained bycompressing the items of data of the five SD pixels from 8-bit data into2-bit data. This data compression is performed by, for example, adaptivedynamic range coding (ADRC). This data aggression may be performed usingany methods other than the ADRC, for example, DPCM (predictive coding),VQ (vector quantization), etc.

When employing ADRC, supposing that a maximum value of the items of dataof the five SD pixels as class taps is MAX, its minimum value is MIN,its dynamic range is DR (=MAX−MIN+1), and its number of re-quantizedbits is P, re-quantized code Qi of P number of bits as compressed datais obtained for each of the five items of SD pixel data ki (i=0-4) bycalculation of the following Equation (1).Qi=[(ki−MIN+0.5)·2^(P) /DR]  (1)

As described above, in the case of compression into 2-bit data, P=2, sothat a 2-bit code Qi is obtained. A high-order bit M and a low-order bitL of a 2-bit code Qi (i=0-4) related to the items of data of the five SDpixels each provide a pixel-value feature.

A DR feature is composed of a 2-bit code (DR class) that indicates towhich one of predetermined four regions each of the dynamic ranges DR ofthe items of data of the five SD pixels as class taps belongs as shownin FIG. 4. That is, when each of the dynamic ranges DR belongs to 0-31,32-63, 64-127, and 128-255, the DR classes are “00”, “01”, “10”, and“11”, respectively. In this case, the high-order bit M and thelower-order bit L of each DR class each provide a DR feature.

FIG. 5 shows 12-bit bit pattern data obtained by interconnecting 2-bitcodes Qi (i=0-4) each constituting the pixel-value feature and a DRclass (2-bit code) constituting the DR feature. Each bit of the 12-bitbit pattern data corresponds to 12 features having numbers 0-11 relatingto each feature included in the basic class configuration. This 12-bitbit pattern data indicates a class in the basic class configuration.

The class detection circuit 104 outputs a class CL that is present in atarget class configuration comprised of one or more features, forexample, six features selected from those ten pixel-value features andtwo DR features. That is, in this case, the class CL is 6-bit patterndata that corresponds to these selected six features. How to select thesix features from the 12 features will be described later.

In FIG. 1, the pixel signal processing apparatus 100 comprises acoefficient memory 105. This coefficient memory 105 is used to storecoefficient data Wi of each class in the above-mentioned target classconfiguration. The coefficient data Wi is used in an estimation equationthat is used in a later-described estimation/prediction calculationcircuit 106. This coefficient data Wi is information for converting anSD signal (525i signal) into an HD signal (1050i signal).

As described above, to convert the 525i signal into the 1050i signal, itis necessary to obtain four pixels of the 1050i signal for each pixel ofthe 525i signal in each of the odd-numbered and even-numbered fields. Inthis case, the four pixels in a unit pixel block of 2-times-2-unit thatconstitutes the 1050i signal in each of the odd-numbered andeven-numbered fields have different respective phase shifts with respectto a predicted center tap.

FIG. 6 shows each phase shift, with respect to a predicted center tapSD0, of four pixels HD1-HD4 in a unit pixel block BK60 of 2-times-2-unitthat constitute the 1050i signal in an odd-numbered field. In it,positions of HD1-HD4 are shifted from a position of SD0 by k1-k4horizontally and m1-m4 vertically, respectively.

FIG. 7 shows each phase shift, with respect to a predicted center tapSD0′, of four pixels HD1′-HD4′ in a unit pixel block BK70 of2-times-2-unit that constitute the 1050i signal in an even-numberedfield. In it, positions of HD1′-HD4′ are shifted from a position of SD0by k1′-k4′ horizontally and m1′-m4′ vertically, respectively.

Therefore, the coefficient memory 105 stores the items of coefficientdata Wi for each combination of a class and output pixels (HD1-HD4,HD1′-HD4′). How to generate the items of coefficient data Wi will bedescribed later.

The coefficient memory 105 receives, as read address information, aclass code CL from the above-mentioned class detection circuit 104. Fromthis coefficient memory 105, the items of coefficient data Wi (i=1˜n)for an estimation equation that correspond to the class code CL are readand supplied to the estimation/prediction calculation circuit 106. Theestimation/prediction calculation circuit 106 calculates items of data yin HD pixels to be made using an estimation equation according to thefollowing estimation equation (2) from the items of data xi for theprediction taps and the items of coefficient data Wi received from thecoefficient memory 105.

$\begin{matrix}{y = {\sum\limits_{i = 1}^{n}{W_{i} \cdot x_{i}}}} & (2)\end{matrix}$

In the Equation (2), “n” indicates the number of prediction taps, whichare selected by the first tap selection circuit 102.

Further, the image signal processing apparatus 100 comprises theestimation/prediction calculation circuit 106 for calculating the itemsof pixel data (pixel data of a target position) in an HD signal to bemade, according to the estimation equation (2), from the items of dataxi of the prediction taps selectively received from the first tapselection circuit 102 and the items of coefficient data Wi received fromthe coefficient memory 105.

As described above, to convert the SD signal into the HD signal, it isnecessary to obtain four pixels (see HD₁-HD₄ in FIG. 6 and HD₁′-HD₄′ inFIG. 7) of the HD signal for each pixel of the SD signal. By thisestimation/prediction calculation circuit 106, items of pixel data aregenerated for each pixel block unit of 2-times-2-unit that constitutesthe HD signal.

That is, this estimation/prediction calculation circuit 106 receives theitems of data xi of the prediction taps that correspond to four pixels(target pixel) in a unit pixel block from the first tap selectioncircuit 102 and the items of coefficient data Wi that correspond to thefour pixels that constitute this unit pixel block from the coefficientmemory 105, thereby calculating four items of pixel data y₁-y₄ thatconstitute the unit pixel block according to the above Equation (2)individually.

Further, the image signal processing apparatus 100 comprises apost-processing circuit 107 for receiving the items of pixel data y₁-y₄of four pixels in a unit pixel block sequentially from theestimation/prediction calculation circuit 106. The post-processingcircuit 107 linear-sequences the items of pixel data y₁-y₄ and outputsthem in a format of the 1050i signal.

The following will describe operations of the image signal processingapparatus 100.

From an SD signal (525i signal) input to the input terminal 101, itemsof data of five SD pixels as class taps which are positioned in theperiphery of four pixels (pixels of target position) in a unit pixelblock that constitutes an HD signal (1050i signal) to be made areselectively taken out at the second tap selection circuit 103.

The data of the class tap selectively taken out at this second tapselection circuit 103 is supplied to the class detection circuit 104.The class detection circuit 104 obtains a 2-bit code Qi by performingADRC processing on each of the items of the data of five SD pixels asclass taps and also obtains a 2-bit code (DR class) from a dynamic rangeDR of the items of the data of five SD pixels as class taps.

Accordingly, the class detection circuit 104 obtains a 12-bit bitpattern data that corresponds to 12 features. In the bit pattern data,each of the 12 bits represents each of the numbers 0-11 relative to thefeatures. Therefore, at the class detection circuit 104, from this12-bit bit pattern data, such bits as to respectively correspond to sixfeatures included in a target class configuration are taken out, therebyobtaining 6-bit bit pattern data as a class CL in the target classconfiguration. This class CL is supplied as read address information tothe coefficient memory 105.

It is to be noted that not all of the 2-bit codes Qi (i=0-4) and the DRclasses but only same of them that correspond to the features in atarget class configuration may be obtained at the class detectioncircuit 104.

When the coefficient memory 105 receives a class CL as read addressinformation, from this coefficient memory 105 correspondingly, items ofthe coefficient data Wi of such estimation equations as to accommodatefour output pixels (HD1-HD4 in an odd-numbered field, HD1′-HD4′ in aneven-numbered field) that correspond to the class code CL are read andsupplied to the estimation/prediction calculation circuit 106.

Further, from the SD signal input to the input terminal 101, the itemsof data (SD pixel data) xi of the prediction taps, which are positionedin the periphery of four pixels (pixels of a target position) in a unitpixel block that constitutes an HD signal to be made, are selectivelytaken out from the first tap selection circuit 102.

The estimation/prediction calculation circuit 106 calculates the itemsof pixel data y₁˜y₄ of the four pixels (pixels of the target position)in the unit pixel block that constitutes the HD signal to be made, usingthe items of data xi of the prediction taps and the items of coefficientdata Wi for the four output pixels received from the coefficient memory105 (see Equation (2)). Then, the items of pixel data y₁˜y₄ of the fourpixels in the unit pixel block that constitutes the HD signalsequentially output from this estimation/prediction calculation circuit106 are supplied to the post-processing circuit 107.

This post-processing circuit 107 linear-sequences the items of the pixeldata y₁˜y₄ of the four pixels in the unit pixel block sequentiallysupplied from the estimation/prediction calculation circuit 106 andoutputs them in the format of the 1050i signal. That is, from thepost-processing circuit 107, the 1050i signal as the HD signal is outputand derived to an output terminal 108.

The following will describe how to generate the items of coefficientdata Wi (i=1˜n), to be stored in the coefficient memory 105, of eachclass in a target class configuration.

First, a normal equation is prepared for calculating coefficient datafor each class in a basic class configuration comprised of all of 12features. This normal equation is obtained by performing learningbeforehand.

A learning method will be described as follows. Before learning, itemsof the coefficient data W₁, W₂, . . . , W_(n) are undetermined in theabove-mentioned Equation (2). Learning is performed on multiple items ofsignal data for each class. If the number of items of the learning datais m, the following equation (3) is set in accordance with Equation (2).Y _(k) =W ₁ ×x _(k1) +W ₂ ×x _(k2) + . . . . +W _(n) ×x _(kn)   (3)

-   -   (k=1, 2 . . . m)        Wherein, “n” indicates the number of prediction taps.

If r>n, none of the items of coefficient data W₁, W₂, . . . , W_(n) canbe determined uniquely, so that an element e_(k) of an error vector e isdefined by the following equation (4) to obtain coefficient data thatminimizes e² in Equation (5). That is, the coefficient data is obtaineduniquely by using the so-called least-squares method.e _(k) =y _(k)−(W ₁ ×x _(k1) +W ₂ ×x _(k2) + . . . +W _(n) ×x_(kn)}  (4)

-   -   (k=1, 2, . . . m)

$\begin{matrix}{e^{2} = {\sum\limits_{k = 1}^{m}e_{k}^{2}}} & (5)\end{matrix}$

By an actual calculation method for obtaining the coefficient data thatminimizes e² in Equation (5), first, as shown in Equation (6), e² ispartially differentiated with respect to the items of coefficient dataWi (i=1˜n) to obtain the items of coefficient data Wi such that apartially differentiated value for each of the “i” values may be 0.

$\begin{matrix}{\frac{\partial e^{2}}{\partial{Wi}} = {{\sum\limits_{k = 1}^{m}{{2\;\left\lbrack \frac{\partial{ek}}{\partial{Wi}} \right\rbrack}\mspace{11mu} e_{k}}} = {\sum\limits_{k = 1}^{m}{2{x_{ki} \cdot e_{k}}}}}} & (6)\end{matrix}$

If Xji and Yi are defined as indicated in the following Equations (7)and (8), Equation (6) can be written in a form of determinant ofEquation (9). This equation (9) is a normal equation for calculating thecoefficient data. By solving this normal equation using a generalsolution such as a sweeping-out method (e.g., Gauss-Jordan's eliminationmethod), the items of coefficient data Wi (i=1˜n) can be calculated.

$\begin{matrix}{X_{ji} = {\sum\limits_{p = 1}^{m}{x_{pi} \cdot x_{pj}}}} & (7) \\{Y_{i} = {\sum\limits_{k = 1}^{m}{x_{ki} \cdot y_{k}}}} & (8) \\{{\begin{bmatrix}X_{11} & X_{12} & \cdots & X_{1n} \\X_{21} & X_{22} & \cdots & X_{2n} \\\cdots & \cdots & \cdots & \cdots \\X_{n1} & X_{n2} & \cdots & X_{nn}\end{bmatrix}\mspace{11mu}\begin{bmatrix}W_{1} \\W_{2} \\\cdots \\W_{n}\end{bmatrix}} = \begin{bmatrix}Y_{1} \\Y_{2} \\\cdots \\Y_{n}\end{bmatrix}} & (9)\end{matrix}$

By considering only six features included in a target classconfiguration of 12 features included in a basic class configuration,normal equations for each class in the basic class configuration havingthe same as those are added up, thereby generating a normal equation forcalculating the coefficient data for each class in the target classconfiguration.

In this case, the number of the classes in the basic class configurationis 2¹²=4096, so that the number of the normal equations for calculatingthe coefficient data for each class in the basic class configuration is4096. On the other hand, the number of the classes in the target classconfiguration, which includes the six features, is 2⁶=64, so that thenumber of the normal equations for calculating the coefficient data ofeach class in the target class configuration is 64.

When only the six features included in the target class configurationare considered, the number of the classes in the basic classconfiguration that have the same as those is 64. That is, a normalequation for calculating coefficient data for each class in the targetclass configuration is generated by adding up the normal equations ofthe 64 classes in the basic class configuration. If the 64 normalequations are given as the following Equations (10-1) through (10-64),they are added up to give such a normal equation as shown in thefollowing Equation (11).

$\begin{matrix}{{\begin{bmatrix}X_{11 - 1} & X_{12 - 1} & \cdots & X_{{1n} - 1} \\X_{21 - 1} & X_{22 - 1} & \cdots & X_{{2n} - 1} \\\cdots & \cdots & \cdots & \cdots \\X_{{n1} - 1} & X_{{n2} - 1} & \cdots & X_{{nn} - 1}\end{bmatrix}\mspace{11mu}\begin{bmatrix}W_{1 - 1} \\W_{2 - 1} \\\cdots \\W_{n - 1}\end{bmatrix}} = \begin{bmatrix}Y_{1 - 1} \\Y_{2 - 1} \\\cdots \\Y_{n - 1}\end{bmatrix}} & \left( 10_{- 1} \right) \\{{\begin{bmatrix}X_{11 - 2} & X_{12 - 2} & \cdots & X_{{1n} - 2} \\X_{21 - 2} & X_{22 - 2} & \cdots & X_{{2n} - 2} \\\cdots & \cdots & \cdots & \cdots \\X_{{n1} - 2} & X_{{n2} - 2} & \cdots & X_{{nn} - 2}\end{bmatrix}\mspace{11mu}\begin{bmatrix}W_{1 - 2} \\W_{2 - 2} \\\cdots \\W_{n - 2}\end{bmatrix}} = \begin{bmatrix}Y_{1 - 2} \\Y_{2 - 2} \\\cdots \\Y_{n - 2}\end{bmatrix}} & \left( 10_{- 2} \right) \\{\mspace{191mu}\vdots} & \; \\{{\begin{bmatrix}X_{11 - 64} & X_{12 - 64} & \cdots & X_{{1n} - 64} \\X_{21 - 64} & X_{22 - 64} & \cdots & X_{{2n} - 64} \\\cdots & \cdots & \cdots & \cdots \\X_{{n1} - 64} & X_{{n2} - 64} & \cdots & X_{{nn} - 64}\end{bmatrix}\mspace{11mu}\begin{bmatrix}W_{1 - 64} \\W_{2 - 64} \\\cdots \\W_{n - 64}\end{bmatrix}} = \begin{bmatrix}Y_{1 - 2} \\Y_{2 - 2} \\\cdots \\Y_{n - 64}\end{bmatrix}} & \left( 10_{- 64} \right) \\\begin{bmatrix}\left( {X_{11 - 1} + X_{11 - 2} + \cdots + X_{11 - 64}} \right) & \left( {X_{12 - 1} + X_{12 - 2} + \cdots + X_{12 - 64}} \right) & \cdots & \left( {X_{{1n} - 1} + X_{{1n} - 2} + \cdots + X_{{1n} - 64}} \right) \\\left( {X_{21 - 1} + X_{21 - 2} + \cdots + X_{21 - 64}} \right) & \left( {X_{22 - 1} + X_{22 - 2} + \cdots + X_{22 - 64}} \right) & \cdots & \left( {X_{{2n} - 1} + X_{{2n} - 2} + \cdots + X_{{2n} - 64}} \right) \\\cdots & \cdots & \cdots & \cdots \\\left( {X_{{n1} - 1} + X_{{n1} - 2} + \cdots + X_{{n1} - 64}} \right) & \left( {X_{{n2} - 1} + X_{{n2} - 2} + \cdots + X_{{n2} - 64}} \right) & \cdots & \left( {X_{{nn} - 1} + X_{{nn} - 2} + \cdots + X_{{nn} - 64}} \right)\end{bmatrix} & (11) \\{\begin{bmatrix}\left( {W_{1 - 1} + W_{1 - 2} + \cdots + W_{1 - 64}} \right) \\\left( {W_{2 - 1} + W_{2 - 2} + \cdots + W_{2 - 64}} \right) \\\cdots \\\left( {W_{n - 1} + W_{n - 2} + \cdots + W_{n - 64}} \right)\end{bmatrix} = \begin{bmatrix}\left( {Y_{1 - 1} + Y_{1 - 2} + \cdots + Y_{1 - 64}} \right) \\\left( {Y_{2 - 1} + Y_{2 - 2} + \cdots + Y_{2 - 64}} \right) \\\cdots \\\left( {Y_{n - 1} + Y_{n - 2} + \cdots + Y_{n - 64}} \right)\end{bmatrix}} & \;\end{matrix}$

Next, the normal equations thus generated for calculating the items ofcoefficient data for each class in the target class configuration aresolved to give the coefficient data for each class in the basic classconfiguration. In this case, these normal equations are solved by ageneral solution such as the sweeping-out method.

FIG. 8 shows a configuration of a normal equation generation apparatus200 for generating a normal equation for calculating coefficient datafor each class in a basic class configuration that is comprised of allof 12 features.

This normal equation generating apparatus 200 comprises an inputterminal 201 to which an HD signal as a tutor signal is input and an SDsignal generation circuit 202 for performing thinning-out processing onthis HD signal horizontally and vertically to thereby obtain an SDsignal as a student signal.

The normal equation generation apparatus 200 also comprises first andsecond tap selection circuits 203 and 204 each selectively taking out,based on the SD signal received from the SD signal generation circuit202, multiple items of data for SD pixels positioned in the periphery ofa target position in the HD signal and outputs them.

These first and second tap selection circuits 203 and 204 areconstituted according to the same way as the first and second tapselection circuits 102 and 103 of the above-mentioned image signalprocessing apparatus 100 shown in FIG. 1. That is, the first tapselection circuit 203 selectively takes out multiple items of data forSD pixels that are used in prediction (refereed to as “predictiontaps”). The second tap selection circuit 204 selectively takes outmultiple items of data for SD pixels that are used in categorization ofclasses (referred to as “class taps”).

The normal equation generation apparatus 200 further comprises a classdetection circuit 205 for detecting a class to which pixel data of atarget position in the HD signal belongs, from the items of data ofclass taps taken out selectively by the second tap selection circuit204. This class detection circuit 205 is constituted according toroughly the same way as the class detection circuit 104 of theabove-mentioned image signal processing apparatus 100 shown in FIG. 1.

In contrast to the class detection circuit 104 which detects a class CLin a target class configuration comprised of, for example, six features,which are selected from 12 features of which a basic class configurationis comprised, this class detection circuit 205 detects a class CLr inthe basic class configuration.

That is, the class detection circuit 205 employs, for example, ADRC tocompress each of the items of data for five SD pixels as class taps froman 8-bit data format into a 2-bit data format, thereby obtaining a2-bitcode Qi (i=0-4) that compose the pixel-value feature. Further, theclass detection circuit 205 obtains a 2-bit code (DR class) according towhich one of predetermined four regions a dynamic range DR of each ofthe items of data for the five SD pixels as class taps belongs (see FIG.4).

The class detection circuit 205 obtains 12-bit bit pattern data which isgiven by interconnecting a 2-bit code Qi (i=0-4) constituting a pixelvalue feature and a DR class (2-bit code) constituting a DR feature (seeFIG. 5) and outputs this 12-bit bit pattern data as a class CLr.

Further, the normal equation generation apparatus 200 further comprisesa delay circuit 206 for time-adjusting an HD signal input to the inputterminal 201 and a normal equation generation unit 207. The normalequation generation unit 207 generates a normal equation (see Equation(9)) for obtaining the items of coefficient data Wi (i=1˜n) for eachclass from each of the items of HD pixel data y as target pixel dataobtained from the HD signal time-adjusted by the delay circuit 206, nnumber of items of the SD pixel data xi as data of prediction tapsselectively taken out from the first tap selection circuit 203corresponding to each of these items of HD pixel data y, and a class CLroutput from the class detection circuit 205 corresponding to each of theitems of HD pixel data y.

In this case, one item of HD pixel data y and the correspondingprediction tap data xi are combined to generate learning data, which isactually generated much for each class between an HD signal as a tutorsignal and an SD signal as the student signal. Accordingly, at thenormal equation generation unit 207, a normal equation for generatingthe items of coefficient data Wi (i=1˜n) is generated for each class.

In this case, further, at the normal equation generation unit 207, anormal equation is generated for each output pixel (see HD1-HD4 of FIG.6, HD1′-HD4′ of FIG. 7). For example, a normal equation that correspondsto HD1 is generated from learning data, which is composed of HD pixeldata y whose value of a shift with respect to a predicted center tap isin the same relationship as that of HD1. As a result, at the normalequation generation unit 207, a normal equation is generated for eachcombination of a class and an output pixel position.

The normal equation generation apparatus 200 still further comprises anormal equation memory 208 for storing data of the normal equationgenerated by the normal equation generation unit 207. A normal equationfor calculating the items of coefficient data Wi (i=1˜n) for each classin a basic class configuration stored in this normal equation memory 208is used to generate the items of coefficient data Wi (i=1˜n) for eachclass in a target class configuration.

The following will describe operations of the normal equation generationapparatus 200 shown in FIG. 8.

An HD signal (1050i signal) as a tutor signal is input to the inputterminal 201. On this HD signal, the SD signal generation circuit 202performs thinning-out processing horizontally and vertically, therebygenerating an SD signal (525i signal) as a student signal.

From this SD signal obtained from the SD signal generation circuit 202,the second tap selection circuit 204 selectively takes out the items ofdata for five SD pixels as class taps positioned in the periphery of atarget position in the HD signal.

The items of data of the class taps selectively taken out from thissecond tap selection circuit 204 are supplied to the class detectioncircuit 205. At the class detection circuit 205, a 2-bit code Qi isobtained by performing ADRC processing on each of the items of data forfive SD pixels as the class taps, a 2-bit code (DR class) is obtainedfrom a dynamic range DR of each of the items of data for the five SDpixels as the class taps, and 12-bit bit pattern data is obtained as aclass CLr by connecting these to each other.

Further, the first tap selection circuit 203 selectively takes out itemsof data xi of the prediction taps positioned in the periphery of atarget position in the HD signal from the SD signal obtained at the SDsignal generation circuit 202.

The normal equation generation unit 207 individually generates normalequations (see Equation (9)) each for obtaining the items of coefficientdata Wi (i=1˜n) for each combination of a class and an output pixelposition, using items of HD pixel data y of each target positionobtained from the HD signal tire-adjusted by the delay circuit 206, theitems of data xi of a prediction tap selectively taken out from thefirst tap selection circuit 203 corresponding to items of the HD pixeldata y of each of the target positions, and a class CLr obtained at theclass detection circuit 205 corresponding to the items of HD pixel datay of each of the target positions. Data of this normal equation isstored in the normal equation memory 208.

FIG. 9 shows a configuration of a coefficient generation apparatus 300for generating items of the coefficient data Wi (i=1˜n) for each classin a target class configuration. The coefficient data Wi is to be storedin the coefficient memory 105 of the image signal processing apparatus100 of FIG. 1.

This coefficient generation apparatus 300 comprises a read only memory(ROM) 301 as a storage unit for storing the items of data of a normalequation for calculating coefficient data for each class in a basicclass configuration. The items of data of this normal equation have beengenerated by, for example, the above-mentioned normal equationgeneration apparatus 200 of FIG. 8.

Further, the coefficient generation apparatus 300 also comprises a maskbit generation unit 302 for generating mask bit pattern data MBP basedon information INF of a target class configuration. It is to be notedthat the information INF indicates one-to-one correspondence between,for example, six features included in a target class configuration and12 features included in a basic class configuration.

The mask bit generation unit 302 generates 12-bit mask bit pattern dataMBP in which a bit corresponding to any features included in the targetclass configuration is set to “1”. FIG. 10B shows data MBP in a casewhere, for example, a target class configuration includes two featuresand these two features indicate a low-order bit L of a 2-bit code Q₂ anda low-order bit L of a DR class, respectively (see FIG. 5).

Further, the coefficient generation apparatus 300 also comprises anormal equation categorization unit 303 for categorizing normalequations each for calculating coefficient data for each class in thebasic class configuration stored in the ROM301 into those thatcorrespond to each class in the target class configuration.

In this case, the normal equation categorization unit 303 considers onlythe features included in the target class configuration and detects suchclasses in the basic class configuration as to have the same as thoseincluded in the target class configuration, to detect each class in thebasic class configuration that correspond to each class in the targetclass configuration, thereby categorizing normal equations for eachclass in the basic class configuration into those that correspond toeach class in the target class configuration.

To detect each class in the basic class configuration that correspond toeach class in the target class configuration, the normal equationcategorization unit 303 uses the above mentioned mask bit pattern dataMBP. It is to be noted that the mask bit generation unit 302 and thenormal equation categorization unit 303 are combined to constitute aclass detection unit.

In this case, a logical product of each bit of the 12-bit data thatrepresents each class (as many as 4096) for the basic classconfiguration shown in FIG. 10A and each bit of the 12-bit mask bitpattern data MBP is calculated. Each class for the basic classconfiguration that have the same bit pattern of a calculation result isthen categorized into the same group, thereby detecting each class inthe basic class configuration as to correspond to each class in thetarget class configuration.

For example, if the mask bit pattern MBP is such as shown in FIG. 10B, aresult of calculation of the logical product will be such as shown inFIG. 10C. In this case, as a result of these calculations, four bitpatterns are obtained such as “000000000000”, “000000000001”,“000001000000”, and “000001000001”. Those classes in the basic classconfiguration that correspond to each of these four bit patterns arecategorized into the same group, so that these groups correspondrespectively to the four classes in the target class configuration, thatis, “00”, “01, 10”, and “11”.

Although FIG. 10 shows a case where the target class configurationincludes two features. The target class configuration may include, forexample, six features, in which case 64 bit patterns are obtained as aresult of calculation. In this case, such classes in the basic classconfiguration as to correspond to each of these 64 bit patterns arecategorized into the same group. Each of these groups of the classes inthe basic class configuration corresponds to each of the 64 classes inthe target class configuration.

The coefficient generation apparatus 300 further a normal equationadding-up unit 304 for adding up, for each category, normal equationsfor calculating the items of coefficient data for each class in a basicclass configuration, which have been categorized by the normal equationcategorization unit 303 so as to correspond to each class in a targetclass configuration, thereby generating a normal equation forcalculating the items of coefficient data for each class in the targetclass configuration. It is to be noted that the above-mentioned classdetection unit constituted of the mask bit generation unit 302 and thenormal equation categorization unit 303 is combined with the normalequation adding-up unit 304 to constitute the normal equation generationunit.

In this case, if the target class configuration includes six features,normal equations that correspond to each of the 64 classes aregenerated. A normal equation of each class is generated by adding up thenormal equations for calculating the items of coefficient data of 64classes in the basic class configuration (see Equations (10-1) through(10-64) and (11)).

It is to be noted that in this case, the normal equation adding-up unit304 adds up normal equations for each of the output pixels (see HD1-HD4of FIG. 6, HD1′-HD4′ of FIG. 7). Therefore, at the normal equationadding-up unit 304, normal equations for calculating the items ofcoefficient data for each class in a target class configuration aregenerated for each combination of the class and output pixel position.

Further, the coefficient generation apparatus 300 still furthercomprises a calculation unit 305 for receiving the items of data fornormal equations generated 2 by the normal equation adding-up unit 304for each combination of the class and the output pixel position andsolving these normal equations to obtain the items of coefficient dataWi for each combination of the class and the output pixel position. Thecoefficient generation apparatus 300 further comprises a coefficientmemory 306 for storing the items of coefficient data Wi obtained by thiscalculation unit 305. The calculation unit 305 solves the normalequations using, for example, the sweeping-out method, thereby obtainingthe items of coefficient data Wi.

The following will describe operations of the coefficient generationapparatus 300 shown in FIG. 9.

Information INF of a target class configuration is supplied to the maskbit generation unit 302. The mask bit generation unit 302 generates12-bit mask bit pattern data MBP in which a bit corresponding to afeature included in the target class configuration is set to “1”. Thisdata MBP is supplied to the normal equation categorization unit 303.

The normal equation categorization unit 303 categorizes normal equationseach for calculating the coefficient data for each class in a basicclass configuration stored in ROM 301 into those that correspond to eachclass in the target class configuration.

For this categorization, the normal equation categorization unit 303considers only the feature included in the target class configurationand detects such classes in the basic class configuration as to have thesame as those to thereby detect the classes in the basic classconfiguration that correspond to each of the classes in the target classconfiguration.

In this case, a logical product of each bit of the 12-bit data thatrepresents each class (as many as 4096) of the basic class configurationand each bit of the 12-bit mask bit pattern data MBP is calculated, sothat the classes in the basic class configuration that have the same bitpattern of a calculation result are categorized into the same group,thereby detecting such classes in the basic class configuration as tocorrespond to each of the classes in the target class configuration.

The normal equation adding-up unit 304 adds up, for each category,normal equations each for calculating the items of coefficient data ofeach class in a basic class configuration which have been categorized bythe normal equation categorization unit 303 so as to correspond to eachclass in a target class configuration, thereby generating a normalequation for calculating the items of coefficient data of each class inthe target class configuration. It is to be noted that data of thenormal equations for calculating each class in the basic classconfiguration is read from the ROM301 and supplied through the normalequation categorization unit 303 to the normal equation adding-up unit304.

Further, in this case, for each of the output pixels (HD1-HD4 of FIG. 6,HD1′-HD4′ of FIG. 7), the normal equations are added up. Therefore, atthe normal equation adding-up unit 304, normal equations for calculatingthe items of coefficient data of each class in a target classconfiguration are generated for each combination of the class and outputpixel position.

Items of data of the normal equations generated by the normal equationadding-up unit 304 for each combination of the class and the outputpixel position are supplied to the calculation unit 305. The calculationunit 305 solves these normal equations to obtain the items ofcoefficient data Wi for each combination of the class and the outputpixel position. The items of coefficient data Wi are stored in thecoefficient memory 306.

In such a manner, it is possible in the coefficient generation apparatus300 shown in FIG. 9 to generate the items of coefficient data Wi to beused in an estimation equation for each combination of a class and anoutput pixel position (HD1-HD4, HD1′-HD4′), which are stored in thecoefficient memory 105 of the image signal processing apparatus 100 ofFIG. 1.

In this case, by storing the normal equations for calculating the itemsof coefficient data for each class in a basic class configurationincluding 12 features in the ROM 301 beforehand, and considering only afeature included in a target class configuration and adding up thenormal equations of such classes in the basic class configuration as tohave the same as those to thereby generate normal equations each forcalculating the items of coefficient data for each class in the targetclass configuration and solving this normal equation, it is possible toefficiently generate the items of coefficient data for each class in thetarget class configuration, thereby generating the items of coefficientdata for each class in an arbitrary class configuration by performinglearning only once. Therefore, when altering the features included in atarget class configuration, it is not necessary to perform learningagain, thereby enabling the coefficient data to be easily generated inshort tire.

As described above, in the image signal processing apparatus 100 shownin FIG. 1, for example, six features are selected from the 12 features,so that classes in a target class configuration including these selectedsix features are categorized.

The following will describe processing for selecting r (which is aninteger) number of the features from n (which is an integer, r<n) numberof the ID features and generating a target class configuration thatincludes the r number of the features.

A flowchart of FIG. 11 shows a procedure for the processing.

At step ST1, “i” is set to 0. At step ST2, in addition to i (which is aninteger) number of the features that have already selected, one of theremaining (n−i) number of the features is selected. Thus, (n−i) numberof class configurations each including (i+1) number of the features aremade.

At step ST3, an arbitrary evaluation value is used to select an optimalclass configuration of the (n−i) number of class configurations. Forexample, this optimal class configuration is selected by the followingprocessing.

First, coefficient data of each class in each of the (n−i) number ofclass configurations is generated. This data is generated using, forexample, the above-mentioned coefficient generation apparatus 300 shownin FIG. 9.

Next, the generated coefficient data of each class is used for eachclass configuration, to convert an image signal that corresponds to anSD signal into an image signal that corresponds to an HD signal usingthe above-mentioned image signal processing apparatus 100 shown inFIG. 1. It is to be noted that the image signal that corresponds to theSD signal is supposed to have been generated by performing thinning-outprocessing horizontally and vertically on an evaluating image signalthat corresponds to the HD signal.

Next, for each class configuration, an evaluation value is obtained onthe basis of a difference for each item of pixel data between theconverted image signal and the evaluating image signal. The followingEquation (12) gives a signal to noise ratio (SNR) as one example of theevaluation value.

$\begin{matrix}{{SNR} = {20\;{\log_{10}\left( \frac{255}{\sqrt{\frac{\sum\limits_{i = 1}^{N}\left( {{yi} - {Yi}} \right)^{2}}{N}}} \right)}}} & (12)\end{matrix}$

In Equation (12), yi indicates an i′th item of the pixel data in theconverted image signal, Yi indicates an i′th item of the pixel data inthe evaluating image signal, and N indicates the number of pixels.

Next, an optimal class configuration is selected on the basis of theevaluation values of the class configurations. If the evaluation valueis an SNR given by Equation (12), such a class configuration that thisSNR is maximized is selected as the optimal class configuration.

In FIG. 11, after processing at step ST3, features, which have been usedin the selected class configuration, are used as the already selectedfeatures at step ST4 and, at step ST5, “i” is increased by 1 (one). Atstep ST6, it is decided whether i=r. If i=r, it means that r number ofthe features included in the target class configuration have beenselected already, so that the processing ends. If “i” is not equal to r,on the other hand, the process returns to step ST2 to repeat the sameprocessing as described above.

FIG. 12 shows a configuration of a target class configuration generationapparatus 400.

This target class configuration generation apparatus 400 comprises aclass configuration generation unit 401 for adding to i (which is aninteger) number of the already selected features a feature selected fromthe remaining (n−i) number of the features to generate (n−i) number ofclass configurations each including the (i+1) number of the features,and a class configuration selection unit 402 for selecting an optimalclass configuration from the (n−i) number of class configurations(generated class configurations) using an arbitrary evaluation value.

Further, this target class configuration apparatus 400 also comprises adecision unit 403 for deciding whether the number of the selectedfeatures is r, a target class configuration output unit 404 for, if thenumber of the selected features is r, outputting information of theselected r number of features as information INF of the target classconfiguration, and an output terminal 405 for outputting thisinformation INF.

In this case, by using the features, which have been used in a classconfiguration selected by the class configuration selection unit 402, asthe already selected features and repeating the above-mentionedoperations by the class configuration generation unit 401 and the classconfiguration selection unit 402 with values for “i” sequentiallyvarying from 0 to r−1, r number of the features are selected, therebygenerating a target class configuration.

The class configuration selection unit 402 includes a coefficientgeneration unit 421, an image signal processing unit 422, an evaluationvalue calculation unit 423, and an evaluation value decision unit 424.The coefficient generation unit 421 generates items of coefficient dataWi for each class in (n−i) number of class configurations generated bythe class configuration generation unit 401. This coefficient generationunit 421 generates the items of coefficient data Wi for each class in(n−i) number of class configurations utilizing, for example, thecoefficient generation apparatus 300 shown in FIG. 9. Accordingly, it ispossible to obtain the items of coefficient data for each class in (n−1)number of the respective generated class configurations by performinglearning only once, thereby improving an efficiency of processing.

The image signal processing unit 422 converts, for each classconfiguration, an image signal that corresponds to an SD signal into animage signal that corresponds to an HD signal using the items ofcoefficient data for each class generated by the coefficient generationunit 421. This image signal processing unit 422 converts an image signalthat corresponds to an SD signal into an image signal that correspondsto an HD signal using the image signal processing apparatus 100 shown inFIG. 1. In this case, the items of coefficient data Wi generated by thecoefficient generation unit 421 are stored in the coefficient memory 105beforehand. Further, the image signal that corresponds to the SD signalis supposed to have been made by performing thinning-out processinghorizontally and vertically on an evaluating image signal Vr thatcorresponds to the HD signal.

The evaluation value calculation unit 423 calculates, for each classconfiguration, an evaluation value based on a difference for each itemof pixel data between an image signal Vo obtained as converted by theimage signal processing unit 422 and the evaluating image signal Vr. Forexample, the evaluation value is an SNR given by the above-mentionedEquation (12). The evaluation value decision unit 424 selects an optimalclass configuration based on an evaluation value of class configurationsobtained by the evaluation value calculation unit 423. If the evaluationvalue is an SNR given by Equation (12), such a class configuration thatthis SNR is maximized is selected as an optimal class configuration.

The following will describe operations of the target class configurationgeneration apparatus 400 shown in FIG. 12.

First, the decision unit 403 sets “i” to 0 and gives an instruction tothe class configuration generation unit 401 to generate n number ofclass configurations each including a feature. In accordance with theinstruction, the class configuration generation unit 401 generates theclass configurations and supplies information of each of these generatedconfigurations to the class configuration selection unit 402.

The class configuration selection unit 402 selects an optimal classconfiguration from the class configurations (generated classconfigurations) generated by the class configuration generation unit401, using an arbitrary evaluation value. In this case, such a classconfiguration that an SD signal can be converted into an HD signal bestwhen classes are categorized on the basis of the evaluation value isselected as the optimal class configuration.

Information of the class configuration selected by the classconfiguration selection unit 402 is supplied to the decision unit 403.The decision unit 403 adds 1 to a value for “i” to provide i=2. If “i”is not equal to r, it supplies the class configuration unit 401 withinformation on the feature included in the class configuration selectedby the class configuration selection unit 402 as information of thealready selected feature, thereby giving an instruction to generate(n−1) number of class configurations each including two features.

The class configuration generation unit 401 generates the classconfigurations in accordance with the instruction and supplies theinformation on each of the generated class configurations to the classconfiguration selection unit 402. The class configuration selection unit402 selects an optimal class configuration from the class configurations(generated class configurations) generated by the class configurationgeneration unit 401.

Information on the class configurations selected by the classconfiguration selection unit 402 is supplied to the decision unit 403.The decision unit 403 adds 1 to the value for “i” to provides i=3. If“i” is not equal to r, it supplies the class configuration unit 401 withinformation on the features included in the class configuration selectedby the class configuration selection unit 402 as information on thealready selected features and gives an instruction to generate (n−2)number of class configurations each including three features.

And so on, until the decision unit 403 decides that i=r, theseoperations are repeated. The decision unit 403, when having decided thati=r, supplies the target class configuration output unit 404 withinformation on the class configuration (information of r number of thefeatures) finally selected by the class configuration selection unit402. This output unit 404 outputs information INF of a target classconfiguration to the output terminal 405.

FIG. 13 shows an example of processing where a target classconfiguration has been generated by actually selecting six features from12 features. A mark, “↓” indicates a feature, which is selected for eachtime.

For a first time, twelve class configurations each including a featurehave been generated and, as a result of evaluation based on SNR, theclass configuration that includes number 6 of the feature has beenselected as an optimal class configuration. For a second time, elevenclass configurations each including two features, i.e., the number 6 ofthe feature plus another feature, have been generated and, as a resultof evaluation based on SNR, the class configuration that includes thenumbers 6 and 2 of the features has been selected as an optimal classconfiguration. For a third time, ten class configurations each includingthree features, i.e., the numbers 6 and 2 of the features plus anotherfeature, have been generated and, as a result of evaluation based onSNR, the class configuration that includes the numbers 6, 2, and 10 ofthe features has been selected as an optimal class configuration.

For a fourth time, nine class configurations each including fourfeatures, i.e., the numbers 6, 2 and 10 of the features plus anotherfeature, have been generated and, as a result of evaluation based onSNR, the class configuration that includes the numbers 6, 2, 10, and 5of the features has been selected as an optimal class configuration. Fora fifth time, eight class configurations each including five features,i.e., the numbers 6, 2, 10 and 5 of the features plus another feature,have been generated and, as a result of evaluation based on SNR, theclass configuration that includes the numbers 6, 2, 10, 5, and 7 of thefeatures has been selected as an optimal class configuration. For asixth time, seven class configurations each including six features,i.e., the numbers 6, 2, 10, 5, and 7 of the features plus anotherfeature, have been generated and, as a result of evaluation based onSNR, the class configuration that includes the numbers 6, 2, 10, 5,7,and 3 of the features has been selected as an optimal classconfiguration.

As a result, the class configuration that includes the numbers 6, 2, 10,5, 7 and 3 of the features has been selected as a target classconfiguration.

As described above, in the target class configuration generationapparatus 400 shown in FIG. 12, an operation of generating (n−i) numberof class configurations each including the already selected “i” numberof features plus a feature selected from (n−i) number of the remainingthe features and an operation of selecting an optimal classconfiguration from these (n−i) number of class configurations using anarbitrary evaluation value are repeated with values for “i” sequentiallyvarying from 0 to r−1 in which the features used in the selected classconfiguration are used as the already selected features, to select the rnumber of the features from the n number of the features and obtain atarget class configuration including the r number of the features. Thisallows an optimal class configuration to be obtained in short timewithout relying on human experiences.

If, in this case, it is wished to generate class configurations byselecting r number of the features from n number of the features, thenumber of possible class configurations becomes nCr=n!/{(n−r)!r!}. Forexample, to generate class configurations by selecting six features from12 features, the number of possible configurations is 12C6=924. Thenumber of class configurations becomes enormous as the numeral n becomeslarge. Therefore, it takes enormous time to evaluate each of the allclass configurations, which is difficult to perform.

However, if the target class configuration generation apparatus 400shown in FIG. 12 is used to generate an optimal class configuration, thenumber of class configurations required for comparison becomesn+(n−1)+(n−2)+, . . . , +(n−r+1). For example, to generate classconfigurations by selecting six features from 12 features, the number ofclass configurations required for comparison is 12+11+10+9+8+7=57, thusresulting in a large decrease as compared to the case of evaluationperformed for each of the all class configurations. Therefore, anoptical class configuration can be obtained in short time.

It is to be noted that it has been confirmed experimentally that rnumber of features are roughly the same between a case where the rnumber of features are selected to thereby generate a target classconfiguration in the target class configuration generation apparatus 400shown in FIG. 12 and a case where a target class configuration includingthe r number of features is obtained by evaluating each of the all classconfigurations.

Also processing in the target class configuration generation apparatus400 of FIG. 12 can be realized by software, an apparatus for whichprocessing is not shown though. In this case, processing for generatinga target class configuration is executed in accordance with a procedureshown by the above-mentioned flowchart of FIG. 11.

In the above-mentioned image signal processing apparatus 100 shown inFIG. 1, for example, six features are selected from 12 features by thetarget class configuration generation apparatus 400 shown in FIG. 12, sothat classes in a target class configuration including these selectedsix features are categorized. As described above, this target classconfiguration provides an optimal class configuration without relying onhuman experiences. Therefore, in the image signal processing apparatus100, an SD signal can be converted into an HD signal by performingconversion processing accompanied by class categorization by use of anoptimal class configuration.

It is to be noted that processing in the image signal processingapparatus 100 of FIG. 1 can be realized by software using such an imagesignal processing apparatus 500 as shown in FIG. 14, for example.

The following will describe the image signal processing apparatus 500shown in FIG. 14. This image signal processing apparatus 500 comprises aCPU 501 for controlling operations of the entire apparatus, a read onlymemory (ROM) 502 for storing a control program of this CPU 501,coefficient data and the like, and a random access memory (RAM) 503which constitutes a working area for the CPU501. These CPU 501, ROM 502,and RAM 503 are each connected to a bus 504.

The image signal processing apparatus 500 also comprises a hard diskdrive (HDD) 505 as an external storage and a floppy (trademark) diskdrive 506. These drive 505 and 506 are each connected to the bus 504.

The image signal processing apparatus 500 further comprises acommunication unit 508 for connecting the apparatus 500 to acommunication network 507 such as the Internet in wired or wirelesscommunication. This communication unit 508 is connected via an interface509 to the bus 504.

Further, the image signal processing apparatus 500 is equipped with auser interface unit. This user interface unit comprises a remote-controlsignal reception circuit 511 for receiving a remote-control signal RMfrom a remote-control transmitter 510 and a display 513 such as acathode ray tube (CRT) and a liquid crystal display (LCD). The receptioncircuit 511 is connected via an interface 512 to the bus 504 and,similarly, the display 513 is connected via an interface 514 to the bus504.

The image signal processing apparatus 500 comprises an input terminal515 for receiving an SD signal and an output terminal 517 for outputtingan HD signal. The input terminal 515 is connected via an interface 516to the bus 504 and, similarly, the output terminal 517 is connected viaan interface 518 to the bus 504.

It is to be noted that instead of storing the control program and thecoefficient data etc. in the ROM502 beforehand as described above, thecontrol program and coefficient data etc. may be downloaded from thecommunication network 507 such as the Internet via the communicationunit 508 and stored in the hard disk drive 505 or the RAM303 and beused. Further, these control program and coefficient data etc. may beprovided in a floppy disk.

Further, beforehand an SD signal to be processed may be recorded in thehard disk drive 505 or downloaded from the communication network 507such as the Internet via the communication unit 508, instead of beinginput through the input terminal 515. Further, instead of orconcurrently with outputting the processed HD signal to the outputterminal 517, it may be supplied to the display 513 to display an imageor stored in the hard disk drive 505 or sent via the communication unit508 to the communication network 507 such as the Internet.

The following will describe a processing procedure for obtaining an HDsignal from an SD signal in the image signal processing apparatus 500shown in FIG. 14, with reference to a flowchart of FIG. 15.

Processing starts at ST11 and, at ST12, one frame or one field of an SDsignal is input into the apparatus through, for example, the inputterminal 515. The SD signal thus input is stored in the RAM503temporarily. It is to be noted that if the SD signal is recorded in thehard disk drive 505 in the apparatus beforehand, the SD signal is readfrom this drive 505 and the read SD signal is stored in the RAM503temporarily.

At step ST13, it is decided whether processing of the entire frame orfield of the SD signal is finished. If finished, the process goes tostep ST14 to end the processing. Otherwise, the process goes to stepST15.

At step ST15, items of data for class taps positioned in the peripheryof a target position in an HD signal are obtained from the SD signal.Based on these items of data of the class taps, a class CL to whichpixel data of the target position in the HD signal belongs is generated.For example, as the items of data for the class taps, the items of datafor five SD pixels are taken out, to generate a class CL in a targetclass configuration that includes six features.

At step ST16, items of data for prediction taps positioned in theperiphery of the target position in the HD signal are obtained from theSD signal. At step ST17, items of coefficient data Wi that correspond tothe class code CL generated at step ST15 and items of data xi for theprediction taps obtained at step ST16 are used to generate items ofpixel data y of the target position in the HD signal based on theestimation equation of Equation (2).

At step ST18, it is decided whether the processing of obtaining pixeldata of the HD signal is finished over all regions of the one frame orfield of the SD signal input at step ST12. If finished, the processreturns to step ST12 to shift to processing of inputting the next oneframe or field of the SD signal. Otherwise, the process returns to stepST15 to shift to processing of the next target position.

By thus performing the processing along the flowchart shown in FIG. 15,it is possible to process pixel data of the SD signal, thereby obtainingpixel data of the HD signal. The HD signal thus obtained through theprocessing is output from the output terminal 517 or supplied to thedisplay 513 where an image due to it is displayed or to the hard diskdrive 505 where it is recorded.

Further, also processing in the normal equation generation apparatus 200of FIG. 8 can be realized by software, an apparatus for which processingis not shown though.

The following will describe a processing procedure for generatingcoefficient data, with reference to a flowchart of FIG. 16.

At step ST31, the processing starts and, at step ST32, one frame orfield of an HD signal as a tutor signal ST is input. At step ST33, it isdecided whether the entire frame or field of the tutor signal ST isfinished. If it is yet to be finished, the process goes to step ST34 togenerate an SD signal as a student signal SS from the tutor signal STinput at step ST32.

At step ST35, items of data for class taps positioned in the peripheryof a target position in the tutor signal ST are obtained from thestudent signal SS. Based on these items of data for the class taps, aclass CLr to which pixel data of the target position in the tutor signalST belongs is generated. In this case, for example, as the items of datafor class taps, items of data for five SD pixels are taken out, togenerate a class CLr in a basic class configuration that includes 12features.

At step ST36, items of data for prediction taps positioned in theperiphery of the target position in the tutor signal ST are obtainedfrom the student signal SS. At step ST37, the class code CLr generatedat step ST35, items of data xi for the prediction taps obtained at stepST36, and the items of pixel data y of the target position in the tutorsignal ST are used to perform addition for the purpose of obtaining anormal equation indicated in Equation (9) (see Equations (7) and (8))for each class.

At step ST38, it is decided whether learning processing is finished overthe entire region of the one frame or field of the tutor signal ST inputat step ST32. If finished, the process returns to step ST32 to input thenext one frame or field of the tutor signal ST. Otherwise, the processreturns to step ST35 to shift to the processing of the next targetposition.

If it is decided that the processing is finished at the above-mentionedstep ST33, the process goes to step ST39 to save in the memory the dataof the normal equation of each class in a basic class configurationgenerated by the above-mentioned addition processing performed at stepST37 and then goes to step ST40 to end the processing.

By thus performing the processing along the flowchart shown in FIG. 16,a normal equation for calculating respective items of the coefficientdata Wi of each class in a basic class configuration is generated by thesame method as that employed in the normal equation generation apparatus200 shown in FIG. 8.

Also, processing in the coefficient generation apparatus 300 of FIG. 9can be realized by software, apparatus for which processing is not shownthough.

The following will describe a processing procedure for generatingcoefficient data, with reference to a flowchart of FIG. 17.

At step ST41, the processing starts and, at step ST42, a target classconfiguration is set. At step ST43, based on information INF of the settarget class configuration, 12-bit mask bit pattern data MBP isgenerated in which a bit corresponding to a feature included in thetarget class configuration is set to “1”.

At step ST44, normal equations for calculating coefficient data for eachclass in the basic class configuration are categorized to the same groupthat corresponds to each class in the target class configuration.Therefore, a logical product of each bit of the 12-bit data thatrepresents the classes (as many as 4096) of the basic classconfiguration and each bit of the 12-bit mask bit pattern data MBP iscalculated, so that the classes in the basic class configuration thathave the same bit pattern of a calculation result are categorized intothe same group, thereby detecting such classes in the basic classconfiguration as to correspond to each class in the target classconfiguration.

At step ST45, for each group, the normal equations for calculating theitems of coefficient data for each class in the basic classconfiguration that have been categorized as to correspond to each classin the target class configuration are added up to generate a normalequation for calculating the coefficient data for each class in thetarget class configuration.

At step ST46, each of the normal equations generated at step ST45 issolved to generate the items of coefficient data Wi for each class inthe target class configuration. The items of coefficient data Wi aresaved in the coefficient memory at step 47, whereupon at step 48, theprocessing ends.

By thus performing the processing along the flowchart of FIG. 17, it ispossible to obtain the coefficient data for each class in the targetclass configuration using the same method as that employed in thecoefficient generation apparatus 300 shown in FIG. 9.

The following will describe an additional embodiment of the presentinvention. FIG. 18 shows a configuration of an image signal processingapparatus 100A therefor. In contrast to the image signal processingapparatus 100 shown in FIG. 1 in which the coefficient data Wi for eachclass in a target class configuration is stored in the coefficientmemory 105, in the image signal processing apparatus 100A shown in FIG.18 coefficient seed data, which is coefficient data in a generationequation for generating the coefficient data Wi for each class in thetarget class configuration, is stored in an ROM and used to generate thecoefficient data Wi. In FIG. 18, components corresponding to those inFIG. 1 are indicated by the same symbols and their detailed descriptionis omitted.

The image signal processing apparatus 100A comprises an ROM 110. In thisROM 110, the coefficient seed data for each class in a target classconfiguration is accumulated beforehand. This coefficient seed data iscoefficient data in a generation equation for generating the coefficientdata Wi to be stored in the coefficient memory 105.

As described above, by an estimation/prediction calculation circuit 106,items of data xi for prediction taps and items of coefficient data Wiread from the coefficient memory 105 are used to calculate items of HDpixel data y to be made, using an estimation of Equation (2).

Items of coefficient data Wi (i=1˜n) in this estimation equation aregenerated by a generation equation having parameters r and z in it asindicated in following Equation (13).W _(i) =w _(i0) +w _(i1) r+w _(i2) z+w _(i3) r ² +w _(i4) rz+w _(i5) z ²+w _(i6) r ³ +w _(i7) r ² z+w _(i8) rz ² +w _(i9) z ³   (13)

In this equation, r is a parameter that determines a resolution and z isa parameter that determines a noise cancellation degree.

The ROM 110 stores items of coefficient seed data wi0- through wi9(i=1˜n), which are coefficient data in this generation equation, foreach combination of a class and a pixel position (see HD1-HD4 of FIG. 6,HD1′-HD4′ of FIG. 7). How to generate this coefficient seed data will bedescribed later.

Further, the image signal processing apparatus 100A comprises acoefficient generation circuit 109 for using items of the coefficientseed data for each class in a target class configuration and values ofthe parameters r and z to generate items of the coefficient data Wi(i=1˜n) in an estimation equation that corresponds to the values of theparameters r and z, using Equation (13), for each combination of theclass and the output pixel position. In this coefficient generationcircuit 109, the items of coefficient seed data wi0 through wi9 areloaded from the ROM 110. Further, this coefficient generation circuit109 is supplied with values of the parameters r and z.

The items of coefficient data Wi (i=1˜n) of each class in the targetclass configuration generated by this coefficient generation circuit 109are stored in the above-mentioned coefficient memory 105. The items ofcoefficient data Wi are generated in this coefficient generation circuit109 in each vertical blanking period, for example. Accordingly, even ifvalues of the parameters r and z are changed by user operations, theitems of coefficient data Wi for each class stored in the coefficientmemory 105 can be changed immediately so as to correspond to thesevalues of the parameters r and z, thereby permitting the user tosmoothly adjust the resolution and the noise cancellation degreethereof.

The other components of the image signal processing apparatus 100A areconstituted and operate similar to those of the image signal processingapparatus 100 shown in FIG. 1.

The following will describe how to generate items of coefficient seeddata wi0 through wi9 (i=1˜n) for each class in the target classconfiguration to be stored in the ROM110.

A normal equation is prepared for calculating respective items of thecoefficient seed data for each class in a basic class configurationwhich is comprised of all of the 12 features. This normal equation isobtained by performing learning beforehand. A learning method will bedescribed.

For the following description, tj (j=0-9) is defined as in Equation(14).t0=1, t1=r, t2=z, t3=r2, t4=rz, t5=z2, t6=r3, t7=r2z, t8=rz2,t9=z3  (14)

By using Equation (14), Equation (13) is rewritten into Equation (15).

$\begin{matrix}{W_{j} = {\sum\limits_{i = 0}^{9}{w_{ij}\mspace{11mu} t_{i}}}} & (15)\end{matrix}$

Finally, an undetermined coefficient wij is obtained by learning. Thatis, multiple items of SD pixel data and HD pixel data are used for eachcombination of a class and an output pixel position to determine acoefficient data that minimizes a square error. This solution isreferred to as “a least-squares method”. Assuming that the number oflearning times is m, a residual error in a k'th (1≦k≦m) items oflearning data is ek, and a total sum of square errors is E, E isexpressed by following Equation (16) using Equations (2) and (13).

$\begin{matrix}\begin{matrix}{E = {\sum\limits_{k = 1}^{m}e_{k}^{2}}} \\{= {\sum\limits_{k = 1}^{m}\left\lbrack {y_{k} - \left( {{W_{1}x_{1K}} + {W_{2}x_{2K}} + \ldots + {W_{n}x_{nK}}} \right)} \right\rbrack^{2}}} \\{= {\sum\limits_{k = 1}^{m}\left\{ {y_{k} - \left\lbrack {{\left( {{t_{0}w_{10}} + {t_{1}w_{11}} + \ldots + {t_{9}w_{19}}} \right)\mspace{11mu} x_{1k}} + \ldots +} \right.} \right.}} \\\left. \left. {\left( {{t_{0}w_{n0}} + {t_{1}w_{n1}} + \ldots + {t_{9}w_{n9}}} \right)\mspace{11mu} x_{nk}} \right\rbrack \right\}^{2} \\{= {\sum\limits_{k = 1}^{m}\left\{ {y_{k} - \left\lbrack {{\left( {w_{10} + {w_{11}r} + \ldots + {w_{19}z^{3}}} \right)\mspace{11mu} x_{1k}} + \ldots +} \right.} \right.}} \\\left. \left. {\left( {w_{n0} + {w_{n1}r} + \ldots + {w_{n9}z^{3}}} \right)\mspace{11mu} x_{nk}} \right\rbrack \right\}^{2}\end{matrix} & (16)\end{matrix}$

In this equation, xik indicates a k'th items of pixel data at an i'thprediction tap position in an SD image and yk indicates a correspondingk'th item of pixel data in an HD image.

According to a solution by means of the least-squares method, such a wijvalue that a partial differentiation due to wij in Equation (16) may be0 is obtained. This is indicated by following Equation (17).

$\begin{matrix}{\frac{\partial E}{\partial w_{ij}} = {{\sum\limits_{k = 1}^{m}{2\mspace{11mu}\left( \frac{\partial e_{k}}{\partial w_{ij}} \right)\mspace{11mu} e_{k}}} = {{- {\sum\limits_{k = 1}^{m}{2\mspace{11mu} t_{j}x_{ik}e_{k}}}} = 0}}} & (17)\end{matrix}$

Then, by defining Xipjq and Yip as indicated in following Equations (18)and (19) respectively, Equation (17) is rewritten into Equation (20)using a matrix.

$\begin{matrix}{X_{ipjq} = {\sum\limits_{k = 1}^{m}{x_{ik}t_{p}x_{jk}t_{q}}}} & (18) \\{Y_{p} = {\sum\limits_{k = 1}^{m}{x_{ik}t_{p}y_{k}}}} & (19) \\{{\left\lbrack \begin{matrix}X_{1010} & X_{1011} & X_{1012} & \cdots & X_{1019} & X_{1020} & \cdots & X_{10{n9}} \\X_{1110} & X_{1111} & X_{1112} & \cdots & X_{1119} & X_{1120} & \cdots & X_{11{n9}} \\X_{1210} & X_{1211} & X_{1212} & \cdots & X_{1219} & X_{1220} & \cdots & X_{12{n9}} \\\vdots & \vdots & \vdots & ⋰ & \vdots & \vdots & ⋰ & \vdots \\X_{1910} & X_{1911} & X_{1912} & \cdots & X_{1919} & X_{1920} & \cdots & X_{19{n9}} \\X_{2010} & X_{2011} & X_{2012} & \cdots & X_{2019} & X_{2020} & \cdots & X_{20{n9}} \\\vdots & \vdots & \vdots & ⋰ & \vdots & \vdots & ⋰ & \vdots \\X_{n910} & X_{n911} & X_{n912} & \cdots & X_{n919} & X_{n920} & \cdots & X_{n9n9}\end{matrix} \right\rbrack\left\lbrack \begin{matrix}w_{10} \\w_{11} \\w_{12} \\\vdots \\w_{19} \\w_{20} \\\vdots \\w_{n9}\end{matrix} \right\rbrack} = \left\lbrack \begin{matrix}Y_{10} \\Y_{11} \\Y_{12} \\\vdots \\Y_{19} \\Y_{20} \\\vdots \\Y_{n9}\end{matrix} \right\rbrack} & (20)\end{matrix}$

This Equation (20) is a normal equation for calculating items ofcoefficient 15 seed data. By solving this normal equation by a generalsolution such as a sweeping-out method (Gauss-Jordan's eliminationmethod), the items of coefficient seed data wi0 through wi9 (i=1˜n) canbe obtained.

FIG. 19 shows a concept of a method of generating a normal equation forcalculation of this coefficient seed data. A plurality of SD signals asstudent signals SS is generated from an HD signal as a tutor signal ST.In this case, by altering a frequency response of a thinning-out filterused to generate SD signals from an HD signal, the SD signals having adifferent resolution are generated.

The SD signals having a different resolution enable coefficient seeddata having a different effect of increasing the resolution to begenerated. For example, if there are an SD signal giving an image havinga large blur and an SD signal giving an image having a small blur, byperforming learning using the SD signal giving the image having thelarge blur, coefficient seed data having a large effect of increasingthe resolution is generated, while by performing learning using the SDsignal giving the image having the small blur, coefficient seed datahaving a small effect of increasing the resolution is generated.

Further, by adding noise to each of the SD signals having differentresolutions, SD signals having the noise are generated. By varying aquantity of noise to be added, SD signals having different quantities ofnoise are generated, thereby generating items of coefficient seed datahaving different effects of canceling noise. For example, if there arean SD signal to which a large quantity of noise is added and an SDsignal to which a small quantity of noise is added, by performinglearning using the SD signal to which the large quantity of noise isadded, coefficient seed data having a large effect of noise cancellationis generated, while by performing learning using the SD signal to whichthe small quantity of noise is added, coefficient seed data having asmall effect of noise cancellation is generated.

The quantity of noise to be added is adjusted by, as indicated in, forexample, following Equation (21), varying G when generating a pixelvalue x′ of a noise-added SD signal by adding noise n to a pixel value xof an SD signal.x′=x+G·n   (21)

For example, by varying the parameter r in nine steps of 0-8 whichparameter changes a frequency response and varying the parameter z innine steps of 0-8 which parameter changes the quantity of noise to beadded, a total 81 kinds of SD signals are generated. By performinglearning between the plurality of SD signals thus generated and an HDsignal, coefficient seed data is generated. These parameters r and zcorrespond to the parameters r and z in the image signal processingapparatus 100A shown in FIG. 18.

Next, by considering only six features included in a target classconfiguration of 12 features included in the basic class configurationand adding up normal equations of each of such class in the basic classconfiguration that have the same as those, a normal equation isgenerated for calculating respective items of coefficient seed data foreach class in the target class configuration. These normal equations areadded up by the same way as in the above-mentioned case of generating ageneration equation for calculating coefficient data for each class in atarget class configuration (see Equations (10-1) through (10-64) and(11)).

Next, the normal equations thus generated for calculation of coefficientseed data for each class in a target class configuration are solved toobtain the coefficient seed data for each class in the target classconfiguration. In this case, these normal equations are solved by ageneral solution such as the sweeping-out method.

FIG. 20 shows a configuration of another normal equation generationapparatus 200A for generating a normal equation for calculatingrespective items of coefficient seed data for each class in a basicclass configuration comprised of all of 12 features. In FIG. 20,components that correspond to those in FIG. 8 are indicated by the samesymbols and their detailed description is omitted.

The normal equation generation apparatus 200A comprises an SD signalgeneration circuit 202A for performing thinning-out processinghorizontally and vertically on an HD signal as a tutor signal input toan input terminal 201 to obtain SD signals as student signals. This SDsignal generation circuit 202A receives the parameters r and z. Inaccordance with the parameter r, a frequency response of a thinning-outfilter used to generate the SD signals from an HD signal is varied.Also, in accordance with the parameter z, a quantity of noise to beadded to each SD signal is varied. The SD signal generated by this SDsignal generation circuit 202A is supplied to a first tap selectioncircuit 203 and a second tap selection circuit 204.

Further, the normal equation generation circuit 200A also comprises anormal equation generation unit 207A. This normal equation generationunit 207A uses each of the items of HD pixel data y as pixel data of atarget position obtained from an HD signal delayed by a time-adjustingdelay circuit 206, items of data xi of prediction taps selectively takenout from the first tap selection circuit 203 corresponding to each ofthese items of HD pixel data y, a class code CLr obtained from a classdetection circuit 205 corresponding to each of the items of HD pixeldata y, and the parameters r and z, to generate a normal equation (seeEquation 20) for obtaining items of coefficient seed data wi0 throughwi9 (i=1−n) for each class in a basic class configuration for eachcombination of a class and an output pixel position. Items of data ofthe normal equations generated by this normal equation generation unit207A are stored in a normal equation memory 208.

The other components of the normal equation generation apparatus 200Ashown in FIG. 20 are constituted the same way as those of the normalequation generation apparatus 200 shown in FIG. 8.

The following will describe operations of the normal equation generationapparatus 200A shown in FIG. 20.

On an HD signal input to an input terminal 201, horizontal and verticalthinning-out processing is performed by the SD signal generation circuit202A to generate SD signals as the student signals. In this case, theparameters r and z are supplied as control signals to the SD signalgeneration circuit 202A, to sequentially generate a plurality of SDsignals having frequency responses and added quantities of noise thatare varied step-wise.

From the SD signals obtained from the SD signal generation circuit 202A,items of data of five SD pixels as class taps positioned in theperiphery of a target position in the HD signal are selectively takenout by the second tap selection circuit 204. These items of data ofclass taps are supplied to the class detection circuit 205. The classdetection circuit 205 performs ADRC processing on the items of data ofthe five SD pixel respectively as the class taps to obtain a 2-bit codeQi, a 2-bit code (DR class) from a dynamic range DR of each of the itemsof data of the five SD pixels as the class taps, and 12-bit pattern databy interconnecting these, as a class CLr.

Further, from the SD signals obtained from the SD signal generationcircuit 202A, the first tap selection circuit 203 selectively takes outitems of data xi of prediction taps positioned in the periphery of thetarget position in the HD signal. The normal equation generation unit207A uses each of the items of HD pixel data y as pixel data of thetarget position obtained from the HD signal time-adjusted by the delaycircuit 206, items of the data xi of prediction taps selectively takenout from the first tap selection circuit 203 corresponding to each ofthese items of HD pixel data y, a class code CLr obtained from the classdetection circuit 205 corresponding to each of the items of HD pixeldata y, and the parameters r and z, to generate a normal equation (seeEquation 20) for generating items of coefficient seed data wi0 throughwi9 (n=1˜9) for each combination of a class and an output pixelposition. Data of these normal equations is stored in the normalequation memory 208.

The items of coefficient seed data wi0 through wi9 (n=1-9) for eachclass in a target class configuration to be stored in the ROM110 in theimage signal processing apparatus 100A of FIG. 18 can be generated inthe above-mentioned coefficient generation apparatus 300 of FIG. 9 as inthe case of generating the items of coefficient data Wi (i=1−n) for eachclass in the target class configuration.

However, in this case, in place of the items of data of normal equationsfor calculating the coefficient data which are generated by the normalequation generation apparatus 200 shown in FIG. 8, items of data of thenormal equations for calculating the coefficient seed data which aregenerated by the normal equation generation apparatus 200A of FIG. 20are stored in the ROM 301.

In this case, at the normal equation adding-up unit 304, normalequations for calculating coefficient seed data for each class in thebasic class configuration that have been categorized by the normalequation categorization unit 303 into the same group so as to correspondto each class in the target class configuration are added up for eachgroup, thereby generating a normal equation for calculating coefficientseed data for each class in the target class configuration.

Further, in this case, for each of the output pixels (HD1-HD4 of FIG. 6,HD1′-HD4′ of FIG. 7), the normal equations are added up. Therefore, atthe normal equation adding-up unit 304, a normal equation forcalculating coefficient seed data for each class in the target classconfiguration is generated for each combination of the class and theoutput pixel position.

Then, data of the normal equation generated by the normal equationadding-up unit 304 for each combination of the class and the outputpixel position is supplied to the operation unit 305. At this operationunit 305, this normal equation is solved to obtain items of coefficientseed data wi0 through wi9 (n=1-9) for each combination of the class andthe output pixel position. These items of coefficient seed data wi0through wi9 are stored in the coefficient memory 306.

In such a manner, using the coefficient generation apparatus 300 shownin FIG. 9, the items of coefficient seed data wi0 through wi9 to bestored in the ROM110 of the image signal processing apparatus 100A ofFIG. 18 are generated for each combination of the class and the outputpixel position.

Thus, storing the normal equations for calculating the coefficient seeddata for each class in a basic class configuration that includes 12features in the ROM301 beforehand, considering only the features in atarget class configuration, detecting such classes in the basic classconfiguration that have the same as those, adding them up to therebygenerate a normal equation for calculating coefficient seed data foreach class in the target class configuration, and solving this normalequation allows the coefficient seed data for each class in the targetclass configuration to be generated, thereby efficiently generating thecoefficient seed data for each class in an arbitrary class configurationby performing learning only once. Therefore, to alter the featuresincluded in a target class configuration, it is not necessary to performlearning again, thereby enabling coefficient seed data to be easilygenerated in short time.

It is to be noted that the processing in the image signal processingapparatus 100A of FIG. 18 can be realized also by software in such animage signal processing apparatus 500 as shown in FIG. 14, for example.

The following will describe a processing procedure for obtaining an HDsignal from an SD signal in the image signal processing apparatus 500shown in FIG. 14, with reference to a flowchart of FIG. 21.

Processing starts at step ST51 and, at step ST52, one frame or one fieldof an SD signal is input into the apparatus through, for example, theinput terminal 515. The SD signal thus input is stored in the RAM503temporarily. It is to be noted that if the SD signal is recorded in thehard disk drive 505 in the apparatus beforehand, the SD signal is readfrom this drive 505 and the read SD signal is stored in the RAM503temporarily.

At step ST53, it is decided whether processing of the entire frame orfield of the SD signal is finished. If finished, the process goes tostep ST54 to end the processing. Otherwise, the process goes to stepST55.

At step ST55, values of the parameters r and z input by a user byoperating the remote-control transmitter 510 are read from, for example,the RAM503. At step ST56, the obtained parameters r and z and thecoefficient seed data for each class are used to generate thecoefficient seed data Wi for the estimation equation (see Equation (2))for each class in accordance with the generation equation (e.g.,Equation (13)).

At step ST57, items of data of class taps positioned in the periphery ofa target position in the HD signal are obtained from the SD signal and,based on these items of data of the class taps, a class CL to whichpixel data of the target position in the HD signal belongs is generated.In this case, for example, as the items of data of the class taps, itemsof data of five SD pixels are taken out, to generate a class CL in atarget class configuration that includes six features.

At step ST58, item of data of prediction taps positioned in theperiphery of the target position in the HD signal are obtained from theSD signal. At step ST59, items of coefficient data Wi that correspond tothe class code CL generated at step ST57 and items of data xi of theprediction taps obtained at step ST58 are used to generate items ofpixel data y of the target position in the HD signal based on theestimation equation of Equation (2).

At step ST60, it is decided whether the processing of obtaining pixeldata of the HD signal is finished over all regions of the one frame orfield of the SD signal input at step ST52. If finished, the processreturns to step ST52 to shift to the processing of inputting the nextone frame or field of the SD signal. Otherwise, the process returns tostep ST57 to shift to the processing of the next target position.

By thus performing the processing along the flowchart shown in FIG. 21,it is possible to process pixel data of the SD signal, thereby obtainingpixel data of the HD signal. The HD signal thus obtained through theprocessing is output from the output terminal 517 or supplied to thedisplay 513 where an image due to it is displayed or to the hard diskdrive 505 where it is recorded.

Further, also processing in the normal equation generation apparatus200A of FIG. 20 can be realized by software, an apparatus for whichprocessing is not shown though.

The following will describe a processing procedure for generatingcoefficient data, with reference to a flowchart of FIG. 22.

Processing starts at step ST61 and, at step ST62, values of theparameters r and z to be used in learning are selected. At step ST63, itis decided whether learning is finished for all combinations of thevalues of the parameters r and z. If not finished, the process goes tostep ST64.

At this step ST64, one frame or field of a known HD signal (a tutorsignal) ST is input. At step ST65, it is decided whether the entireframe or field of the HD signal is processed. If finished, the processreturns to step ST62 to select the next values of the parameters r and zand repeat the same processing as the above. Otherwise, the process goesto step ST66.

At step ST66, from the tutor signal ST input at step ST64, the SD signalis generated as a student signal SS. The SD signal has a frequencyresponse and an added quantity of noise that correspond to the values ofthe parameters r and z selected at step ST62.

At step ST67, from the student signal SS, items of data of class tapspositioned in the periphery of a target position in the tutor signal STare obtained and, based on these items of data of class taps, a classCLr to which pixel data of the target position in the tutor signal STbelongs is generated. For example, as the items of data of class taps,items of data of five SD pixels are taken out to generate a class CLr ina basic class configuration that includes 12 features.

At step ST68, from the student signal SS, items of data of predictiontaps positioned in the periphery of the target position in the tutorsignal ST are obtained. At step ST69, the class code CLr generated atstep ST67, items of data xi of the prediction taps obtained at stepST68, the values of the parameters r and z selected at step ST62, anditems of pixel data y of the target position in the tutor signal ST areused to perform addition for the purpose of obtaining the normalequation indicated by Equation (20) (see Equations (18) and (19)).

At step ST70, it is decided whether learning processing is finished overall regions of the pixel data of the one frame or field of the tutorsignal ST input at step ST64. If finished, the process returns to stepST64 to input the next one frame or field of the tutor signal ST andrepeat the same processing as the above. Otherwise, the process returnsto step ST67 to shift to the processing of the next target position.

When it is decided at step ST63 that the learning is finished on all ofcombinations of the values of the parameters r and z, the process goesto step ST71. At this step ST71, items of data of the normal equationfor each class in the basic class configuration generated by theaddition processing at the above-mentioned step ST69 are saved in amemory and then, at step ST72, the processing ends.

By thus performing the processing along the flowchart shown in FIG. 22,it is possible to generate a normal equation for calculating coefficientseed data for each class using the same method as that employed in thenormal equation generation apparatus 200A shown in FIG. 20.

Although in the above embodiments, the basic class configuration hasincluded 12 features, the present invention is not limited to it.Although each of the features has been represented by one bit, thepresent invention can be applied similarly to a case where each of thefeatures is represented by two bits or more.

Although the above embodiments have been described in an example wherethe SD signal is converted into the HD signal, the present invention isnot limited to it and can be applied to any other cases where a firstimage signal is converted into a second image signal through conversionprocessing accompanied by class categorization.

Although in the above embodiments, an SNR indicated by Equation (12) isused as an evaluation value, the evaluation is not limited to it. Inshort, any evaluation value is acceptable as far as, when the classesare categorized on the basis of it, such a class configuration that theSD signal can be converted into the HD signal best is selected as anoptimal class configuration.

Although the above embodiments have been described in a case where theinformational signal is an image signal, the present invention is notlimited to it. The present invention can be applied similarly to, forexample, a case where the informational signal is an audio signal.

While the foregoing specification has described preferred embodiment(s)of the present invention, one skilled in the art may make manymodifications to the preferred embodiment without departing from theinvention in its broader aspects. The appended claims therefore areintended to cover all such modifications as fall within the true scopeand spirit of the invention.

1. A coefficient generation apparatus for generating any one ofcoefficient data for an estimation equation and coefficient seed data,said estimation equation being used for converting a first informationalsignal comprised of multiple items of informational data into a secondinformational signal comprised of multiple items of informational data,said coefficient seed data being coefficient data in a generationequation for generating the coefficient data for the estimationequation, said apparatus comprising: a storage unit configured to storea normal equation for calculating any one of the coefficient data forthe estimation equation and the coefficient seed data for each class ina basic class configuration comprised of all of plural features of thefirst informational signal; a normal equation generation unit configuredto generate a normal equation for calculating any one of the coefficientdata for the estimation equation and the coefficient seed data for eachclass in the target class configuration based on information of a targetclass configuration comprised of at least arbitrary one of said pluralfeatures of the first informational signal; and a calculation unitconfigured to solve the normal equation, which is generated by thenormal equation generation unit, and to calculate any one of thecoefficient data for the estimation equation and the coefficient seeddata for each class in the target class configuration to calculate foreach class any one of the coefficient data for the estimation equationand the coefficient seed data.
 2. The coefficient generation apparatusaccording to claim 1, wherein the normal equation generation unitconsiders, based on information of the target class configurationcomprised of at least arbitrary one of said plural features of the firstinformational signal, only the feature included in the target classconfiguration and adds up the normal equation, which is stored in thestorage unit, of a class in the basic class configuration, said classhaving the same feature, thereby generating a normal equation forcalculating any one of the coefficient data for the estimation equationand the coefficient seed data for each class in the target classconfiguration.
 3. The coefficient generation apparatus according toclaim 1, wherein the normal equation generation unit comprises; a classdetection unit configured to consider, based on information of thetarget class configuration, only the feature included in the targetclass configuration, and to detect a class in the basic classconfiguration, said class having the same feature, to detect the classin the basic class configuration that corresponds to each class in thetarget class configuration; and a normal equation adding-up unitconfigured to add up, for each class in the target class configuration,the normal equation of the class in the basic class configuration thatcorresponds to each class in the target class configuration, said classin the basic class configuration being detected by the class detectionunit, to obtain the normal equation for calculating any one of thecoefficient data for the estimation equation and the coefficient seeddata for each class in the target class configuration.
 4. Thecoefficient generation apparatus according to claim 3, wherein the basicclass configuration includes n number of the features and each class inthe basic class configuration is indicated by n-bit data whose each bitindicates the feature, said n being an integer; and wherein the classdetection unit comprises: a mask bit generation sub-unit configured togenerate n-bit mask bit pattern data whose a bit corresponding to thefeature included in the target class configuration is set to “1”; and aclass detection sub-unit configured to calculate for each bit a logicalproduct of the n-bit data representing each class in the basic classconfiguration and the mask bit pattern data generated by the mask bitgeneration sub-unit, and to categorize the class having the same bitpattern of a calculation result in the basic class configuration intothe same group, to detect the class in the basic class configurationthat corresponds to each class in the target class configuration.
 5. Acoefficient generation method for generating any one of coefficient datafor an estimation equation and coefficient seed data, said estimationequation being used for converting a first informational signalcomprised of multiple items of informational data into a secondinformational signal comprised of multiple items of informational data,said coefficient seed data being coefficient data in a generationequation for generating the coefficient data for the estimationequation, said method comprising the steps of: preparing a normalequation for calculating any one of the coefficient data for theestimation equation and the coefficient seed data for each class in abasic class configuration comprised of all of plural features of thefirst informational signal; based on information of a target classconfiguration comprised of at least arbitrary one of said pluralfeatures of the first informational signal, generating a normal equationfor calculating any one of the coefficient data for the estimationequation and the coefficient seed data for each class in the targetclass configuration; and solving the generated normal equation forcalculating any of the coefficient data for the estimation equation andthe coefficient seed data for each class in the target classconfiguration to calculate for each class any one of the coefficientdata for the estimation equation and the coefficient seed data for eachclass in the target class configuration.
 6. The coefficient generationmethod according to claim 5, wherein the step of generating the normalequation includes sub-steps of considering, based on information of thetarget class configuration comprised of at least arbitrary one of saidplural features of the first informational signal, only the featureincluded in the target class configuration, adding up the normalequation of a class in the basic class configuration, said class havingthe same feature, and generating a normal equation for calculating anyone of the coefficient data for the estimation equation and thecoefficient seed data for each class in the target class configuration.7. A recording medium including a program for commanding a computer toexecute a coefficient generation method for generating any one ofcoefficient data for an estimation equation and coefficient seed data,said estimation equation being used for converting a first informationalsignal comprised of multiple items of informational data into a secondinformational signal comprised of multiple items of informational data,said coefficient seed data being coefficient data in a generationequation for generating the coefficient data for the estimationequation, said method comprising the steps of: preparing a normalequation for calculating any one of the coefficient data for theestimation equation and the coefficient seed data for each class in abasic class configuration comprised of all of plural features of thefirst informational signal; based on information of a target classconfiguration comprised of at least arbitrary one of said pluralfeatures of the first informational signal, generating a normal equationfor calculating any one of the coefficient data for the estimationequation and the coefficient seed data for each class in the targetclass configuration; and solving the generated normal equation forcalculating any one of the coefficient data for the estimation equationand the coefficient seed data for each class in the target classconfiguration to calculate any one of the coefficient data for theestimation equation and the coefficient seed data for each class in thetarget class configuration.
 8. A coefficient generation apparatus forgenerating any one of coefficient data for an estimation equation andcoefficient seed data, said estimation equation being used forconverting a first informational signal comprised of multiple items ofinformational data into a second informational signal comprised ofmultiple items of informational data, said coefficient seed data beingcoefficient data in a generation equation for generating the coefficientdata for the estimation equation, said apparatus comprising: storagemeans for storing a normal equation for calculating any one of thecoefficient data for the estimation equation and the coefficient seeddata for each class in a basic class configuration comprised of all ofplural features of the first informational signal; normal equationgeneration means for, based on the information of a target classconfiguration comprised of at least arbitrary one of said pluralfeatures of the first informational signal, generating a normal equationfor calculating any one of the coefficient data for the estimationequation and the coefficient seed data for each class in the targetclass configuration; and calculation means for solving the normalequation, which is generated by the normal equation generation means,and for calculating any one of the coefficient data for the estimationequation and the coefficient seed data for each class in the targetclass configuration to calculate for each class any one of thecoefficient data for the estimation equation and the coefficient seeddata.